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Big data-driven theory building: Philosophies, guiding principles, and common traps

Kar, A.K.; Angelopoulos, S.; Rao, H.R.

Authors

A.K. Kar

H.R. Rao



Abstract

While data availability and access used to be a major challenge for information systems research, the growth and ease of access to large datasets and data analysis tools has increased interest to use such resources for publishing. Such publications, however, seem to offer weak theoretical contributions. While big data-driven studies increasingly gain popularity, they rarely introspect why a phenomenon is better explained by a theory and limit the analysis to data descriptive by mining and visualizing large volumes of big data. We address this pressing need and provide directions to move towards theory building with Big Data. We differentiate based on inductive and deductive approaches and provide guidelines how may undertake steps for theory building. In doing so, we further provide directions surrounding common pitfalls that should be avoided in this journey of Big-Data driven theory building.

Citation

Kar, A., Angelopoulos, S., & Rao, H. (2023). Big data-driven theory building: Philosophies, guiding principles, and common traps. International Journal of Information Management, Article 102661. https://doi.org/10.1016/j.ijinfomgt.2023.102661

Journal Article Type Article
Acceptance Date Apr 28, 2023
Online Publication Date May 19, 2023
Publication Date 2023
Deposit Date May 19, 2023
Publicly Available Date Nov 20, 2024
Journal International Journal of Information Management
Print ISSN 0268-4012
Electronic ISSN 1873-4707
Publisher Elsevier
Peer Reviewed Peer Reviewed
Article Number 102661
DOI https://doi.org/10.1016/j.ijinfomgt.2023.102661
Public URL https://durham-repository.worktribe.com/output/1172184

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This file is under embargo until Nov 20, 2024 due to copyright restrictions.




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